Processes an answer similar to Eval AI copied from https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a8

Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`SiglipEncoderLayer`]. Args:

Resize the input image so that its longest side and shortest side are within a specified range, ensuring that both sides are divisible by a specif

Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config

r""" [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to instantiate a Siglip model acc

r""" Constructs a SigLIP image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the

Multihead Attention Pooling.

Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for ima

r""" Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor. [`SiglipProcessor`] o

r""" This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a Siglip text encoder acco

Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTo

r""" This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a Siglip vision encoder

Embeds scalar timesteps into vector representations.

Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequ

r""" This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a Qwen2 model according to the

r""" This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a Qwen2 model according to the

Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention` as the weights of the module stays

Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding. Same with GPT2Tokenizer, this tokenizer has been trained to treat space

Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level Byte-Pair-Encoding. Same with GPT